Scale-Optimized Textons for Image Categorization and Segmentation

Texton is a representative dense visual word and it has proven its effectiveness in categorizing materials as well as generic object classes. Despite its success and popularity, no prior work has tackled the problem of its scale optimization for a given image data and associated object category. We propose scale-optimized textons to learn the best scale for each object in a scene, and incorporate them into image categorization and segmentation. Our textonization process produces a scale-optimized codebook of visual words. We approach the scale-optimization problem of textons by using the scene-context scale in each image, which is the effective scale of local context to classify an image pixel in a scene. We perform the textonization process using the randomized decision forest which is a powerful tool with high computational efficiency in vision applications. Our experiments using MSRC and VOC 2007 segmentation dataset show that our scale-optimized textons improve the performance of image categorization and segmentation.